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Attribution of ocean temperature change to anthropogenic and natural forcings using the temporal, vertical and geographical structure
We examine whether significant changes in ocean temperatures can be detected in recent decades and if so whether they can be attributed to anthropogenic or natural factors. We compare ocean temperature changes for 1960–2005 in four observational datasets and in historical simulations by atmosphere-o...
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Published in: | Climate dynamics 2019-11, Vol.53 (9-10), p.5389-5413 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | We examine whether significant changes in ocean temperatures can be detected in recent decades and if so whether they can be attributed to anthropogenic or natural factors. We compare ocean temperature changes for 1960–2005 in four observational datasets and in historical simulations by atmosphere-ocean general circulation models (AOGCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5). Observations and CMIP5 models show that the upper 2000 m has warmed with a signal that has a well-defined geographical pattern that gradually propagates to deeper layers over time. Greenhouse gas forcing has contributed most to increasing the temperature of the ocean, a warming which has been offset by other anthropogenic forcing (mainly aerosols), and volcanic eruptions which cause episodic cooling. By characterizing the ocean temperature change response to these forcings we construct multi-model mean fingerprints of time-depth changes in temperature and carry out two detection and attribution analysis. We consider first a two-signal separation into anthropogenic and natural forcings. Then, for the first time, we consider a three signal separation into greenhouse gas, anthropogenic aerosols and natural forcings. We show that all three signals are simultaneously detectable. Using multiple depth levels decreases the uncertainty of the results. Limiting the observations and model fields to locations where there are observations increases the detectability of the signal. |
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ISSN: | 0930-7575 1432-0894 |
DOI: | 10.1007/s00382-019-04910-1 |